Analog active noise cancellation (ANC) systems suffer from a number of problems. Specifically, they are prone to acoustic feedback, and they do not provide as high a degree of cancellation for periodic or other quasi-stationary signals as can be realized with a digital signal processing (DSP) enhanced analog ANC system.
Analog ANC systems are also difficult to adjust (or “tune”) for different headset designs and also in a production environment where normal production variations in transducers and listening device assembly increase the likelihood of acoustic feedback.
Further, current analog ANC techniques address only part of the noise cancellation that is needed by users in high noise environments. Specifically, analog ANC provides noise cancellation at predominantly low frequencies (below 1500 Hz to 2000 Hz).
Fully digital ANC systems are possible. However, group delay or latency is induced by analog to digital (A/D) conversion, digital to analog conversion and digital processing associated with DSP systems. Further, due to power consumption, they are not practical in many portable applications.
U.S. Pat. Nos. 5,475,761, 5,699,436, and 5,815,582 by Noise Cancellation Technologies (NCT) disclose methods of digital ANC. using a combination of both feedback and feed-forward methods. The methods employ DSP to perform ANC. However, due to the inherent delay in the DSP, they are not practical for most applications when low-power, low-cost, and small-size constraints are applied. There are many other similar DSP-based systems that suffer from the same delay problem, for example, the systems disclosed in U.S. Pat. Nos. 6,418,227 B1, 5,991,418 and 5,940,519 by Kuo et al. from Texas Instruments Inc.
U.S. Pat. Nos. 6,069,959 and 6,118,878 by NCT disclose fully analog solutions to the ANC problems. Specifically, as U.S. Pat. No. 6,118,878 explains, significant tuning and adaptation of the system parameters are necessary to avoid instability and artifacts. However, the patent suggests that the tuning can be implemented using analog components and methods.
DSP-controlled ANC systems have tried to address the difficult problem of tuning of the analog ANC systems through the use of CPUs and signal processing methods. For example, U.S. Pat. No. 5,440,642 by Denenberg et al. discloses DSP techniques that can control ANC system parameters, such as loop gain and loop filter frequency response. U.S. Patent application Publication No. 20040037430 A1 uses DSP techniques (LMS adaptation) to control the secondary path typically used in the filtered-X LMS algorithm. U.S. Pat. No. 4,965,832 uses DSP control of a feed-forward ANC system to control the loop-gain and the loop-filter bandwidth. U.S. Pat. No. 6,278,786 B1 uses DSP to not only control the loop-gain but also to provide an acoustic signal (to be added to the analog ANC anti-noise) that will cope with tonal noises more effectively.
Subband adaptive filters (SAFs) become an interesting and viable option for many adaptive systems. The SAF approach uses a filterbank to split the fullband signal input into a number of frequency bands, each serving as input to an adaptive filter. This subband decomposition greatly reduces the update rate and the length of the adaptive filters resulting in much lower computational complexity.
To be able to employ powerful SAF method for ANC, one has to tackle a processing delay issue.
To reduce the processing delay in the SAFs, U.S. Pat. No. 5,329,587 by Morgan et al. has introduced a method of reconstructing the subband filter back into time-domain. Starting with adapted subband filters, they first transform the SAFs into the frequency-domain (using an FFT), appropriately stack the results, and inverse transform them back into time-domain to obtain a time-domain adaptive filter. The time-domain filter is then used to implement time-domain adaptive filtering. The details of their technique are also reported in a research paper Morgan et. al. (“A delayless subband adaptive filter structure”, IEEE Trans. on Signal Proc., Vol. 43, pp. 1819-1830, Aug. 1995.) that offers a good survey of previous efforts on low-delay adaptive systems. Let us call this method DFT-1 Stacking as disclosed in J. Huo et al. (“New weight transform schemes for delayless subband adaptive filtering”, in Proc. of IEEE Global Telecom. Conf., pp. 197-201, 2001). After analyzing Morgan's method in by J. Huo et al., they offer two variations to the method (known as “DFT-2 Stacking” and “DFT-FIR Stacking”) to improve the performance. These methods are all based on DFT, proper stacking, and inverse DFT. In DFT-FIR, a convolution with a synthesis filter after DFT is also added. Moreover in L. Larson et al. (“A new subband weight transform for delayless subband adaptive filtering structures”, in Proc. of IEEE DSP workshop, pp. 201-206), a Linear Weight Transform method is introduced. The method employs a linear matrix transformation of the subband filters using both analysis and synthesis filters to recover the time-domain adaptive filter. In yet another set of works following Morgan's method, a different method is proposed that employs the Hadamard transform to reconstruct the time-domain filter (N. Hirayama and H. Sakai, “Analysis of a delayless subband adaptive filter”, in Proc. of ICASSP, pp. 2329-2332, 1997; and N. Hirayama et al., “Delayless subband adaptive filtering using the hadamard transform”, IEEE Trans. on Signal Proc., Vol. 47, No. 6, pp. 1731-1734, June 1999).
In a series of research paper presented from 1997 to 1999, Merched et al. present methods of transferring the SAFs to time-domain (R. Merched et al. “A Delayless alias-free subband adaptive filter structure”, in Proc. of IEEE Int. Symp. On Circuits and Systems, pp. 2329-2332, Jun. 9-12, 1997; P. S. R. Diniz et al. “Analysis of a delayless subband adaptive filter structure”, in Proc. of ICASSP, pp. 1661-1664, 1998; R. Merched et al. “A new delayless subband adaptive filter structure”, IEEE Trans. on Signal Proc., Vol. 47, No. 6, pp. 1580-1591, June 1999). Their methods are designed only for maximally decimated (QMF) PR filterbanks, and constraints the filterbank prototype filter to be a Nyquist(K) filter (where K represents number of subbands). As a result, the SAFs become simple fractional delay filters. They also use a polyphase fiterbank to reconstruct the time-domain adaptive filter.
U.S. Pat. No. 6,661,895 B1 by Jin et al. discloses a zero-delay SAF system. They discard the initial segment of each SAF to obtain a “forward filter”. The estimated (time-domain) echo signal is generated by filtering the reference signal through the subband forward filters and then applying subband reconstruction. The time-domain echo cancelled signal goes through another separate time-domain LMS filter to compensate for the discarded initial segments of subband adaptive filters. The method however has a fundamental problem: the time-domain LMS filter has to model a potentially non-causal filter. This is not practically possible.
Over-sampled subband adaptive filters (OS-SAF) offer many advantages over time-domain adaptive algorithms. However, OS-SAF systems may introduce a group delay or latency that is too high for some applications. The conversion from analog to digital and back again, use of anti-aliasing and anti-imaging filters, as well as the digital processing introduces this delay. Reducing the delay of OS-SAF systems by increasing the sampling rate is not practical in many applications because of power consumed and specialized hardware required.
Also, in the conventional OS-SAF systems, the primary input signal goes through analysis and synthesis stages of the over-sampled filterbank. Often, perfect reconstruction (PR) is not practical and only a near PR performance is achieved. As a result, the primary signal may be distorted. To minimize distortions, longer analysis windows have to be employed which further increases the delay and add extra computation cost.
Further, in the conventional OS-SAF systems, the effect of analysis filter band edges and under-modeling errors limit the system performance.